Wind power is considered one of the cleanest, most environmentally friendly energy sources presently available, and wind turbines have gained increased attention in this regard. A modern wind turbine typically includes a tower, a generator, a gearbox, a nacelle, and one or more rotor blades. The rotor blades capture kinetic energy from wind using known foil principles and transmit the kinetic energy through rotational energy to turn a shaft coupling the rotor blades to a gearbox, or if a gearbox is not used, directly to the generator. The generator then converts the mechanical energy to electrical energy that may be deployed to a utility grid.
Such wind turbines are typically located in a wind farm spread across a specific geographical region such that the wind passing over the region causes the blades associated with the wind turbines to rotate. Traditionally, wind farms are controlled in a decentralized fashion to generate power such that each turbine is operated to maximize local power output and to minimize impacts of local fatigue and extreme loads. However, in practice, such independent optimization of the wind turbines ignores farm-level performance goals, thereby leading to sub-optimal performance at the wind farm-level. For example, independent optimization of the wind turbines may not account for aerodynamic interactions such as wake effects between neighboring turbines within the wind farm that may affect a farm-level power output.
Typically, wake effects include a reduction in wind speed and increased wind turbulence at a downstream wind turbine due to a conventional operation of an upstream wind turbine. The reduced wind speed causes a proportional reduction in a power output of the downstream wind turbine. Moreover, the increased turbulence increases the fatigue loads placed on the downstream wind turbine. Several studies have reported a loss of more than 10% in the annual energy production (AEP) of the wind farm owing to the wake effects between neighboring independently optimized wind turbines within the wind farm.
Accordingly, some currently available approaches attempt to optimize power generation at the wind farm-level by mitigating an impact of the wake effects through a coordinated control of the wind turbines in the wind farm. Typically, mitigating the wake effects involves accurately modeling the wake effects experienced at different wind turbines in the wind farm. For example, empirical or semi-empirical thrust-based, and/or high fidelity physics-based models may be used to model the wake effects between the aerodynamically interacting wind turbines in the wind farm.
Conventionally, the empirical or semi-empirical models (engineering wake models) are generated based on field-experiment data and/or historical wind information. Accordingly, these models may be used to design the layouts of wind farms so as to optimize one or more performance goals before installation of the wind turbines. Alternatively, these models may be used to optimize performance of the wind farm subsequent to the installation.
One optimization approach, for example, employs the engineering wake models to determine control settings for the wind turbines. Particularly, the engineering wake models determine the control settings so as to operate upstream turbines at lower efficiencies, which in turn, allows for greater energy recovery at the downstream turbines. Another approach uses the engineering wake models for adjusting a yaw alignment of the upstream turbines relative to an incoming wind direction to steer the resulting wake effects away from the downstream turbines.
However, the conventional engineering models do not account for prevailing wind inflow and other ambient conditions such as atmospheric boundary layer stability and longitudinal turbulence intensity. As the ambient conditions over the wind farm tend to change frequently, the wake models estimated using the engineering wake models may be inaccurate for use during real-time implementation. Inaccurate modeling of the wake conditions, in turn, may result in use of incorrect control settings for the wind turbines in the wind farm. Thus, the conventional optimization approaches using the engineering wake models usually provide only a marginal improvement in the farm-level performance output.
Another optimization approach employs hi-fidelity wake models, for example, based on computational fluid dynamics modeling. Such wake models may provide greater accuracy in modeling wake interactions. The hi-fidelity models entail measurement and analysis of a wide variety of parameters that necessitate additional instrumentation, complex computations, and associated costs. The cost and complexity associated with the hi-fidelity models, therefore, may preclude wider use of these models in every turbine in the wind farm and/or for real time optimization of wind farm operations.
Still further approaches includes optimizing the control set points sent to upstream turbines, that are found to wake other downstream turbines, based on predictions from a wake model, in order to mitigate the wind speed deficit due to wake at downstream turbines. Such models are typically referred to as a pair-wise model that predicts the velocity deficit ratio between the upstream and the downstream turbines. One of the inputs to such models is the line joining the hub center of the upstream turbine and the center of the wake at the downstream turbine rotor plane and the line connecting the rotor plane centers of the two turbines. Previous pair-wise models were offline regression models, where the angular offset is calculated based on the assumption that the wake center coincides with the average wind direction projected at the downstream turbine rotor plane. Such an assumption, however, does not take into account the meandering effect of the wake due to lateral and vertical components of wind speed. Hence, in such models, the wake offset angle, due to lack of consideration of the meandering effect, might indicate that a turbine is waked while in reality it is not and vice versa. This in turn might penalize the upstream turbine control set-point to be conservative or aggressive, when the wake predictions are inaccurate. Furthermore, as the pair-wise regression model is an offline model, and is the same model irrespective of the terrain, land use around the turbine pair, and/or ambient conditions such as the turbulence level or atmospheric boundary layer state.
Accordingly, there is a need for an online adaptive farm-level wake model that takes into account the land use, atmospheric conditions and/or ambient conditions around the turbine pair, in addition to the meandering component between the waked pair of wind turbines.